These set of slides were presented at the BEP Seminar "Targeting in Development Projects: Approaches, challenges, and lessons learned" held last Oct. 2, 2023 in Cairo, Egypt
2. Policy and Research Intersection on
Targeting
Bridging Evidence and Policy Seminar Series
Sikandra Kurdi
Egypt Country Program
IFPRI
Cairo | October 2, 2023
3. •Choose targeting approach
•Select specific policy variables
(i.e. cutoffs, weights)
•Communicate approach to
stakeholders
•Evaluate targeting relative to
objectives
•Evaluate relationship between
targeting and impact
•Describe theoretical trade-offs
•Use theory to predict behavioral
responses
•Use data to develop formulas for
predicting poverty or other
characteristics
•Set and prioritize objectives
Policy Research
Policy
Research
Policy/ Research Intersection on Targeting
4. IFPRI Research on Targeting in Egypt
• IFPRI targeting evaluation of Takaful in 2017
• Inclusion and exclusion errors similar to other large scale PMT programs
• Highest exclusion errors in urban areas where outreach had not been
intensive
• Takaful impacts were higher among older households
• Analysis using ELMPS panel data highlights that household PMT score is higher with age
even after controlling for consumption
• Supports recent change in policy to have higher cut-offs for older households
• Ongoing evaluation of overlap in targeting between Takaful and EFB
• EFB impacts are higher on non-Takaful beneficiaries
5. State of the Global Literature on Targeting
Recent literature review Banerjee, Hanna, Olken, Lisker (August 2023)
• Experimentation with new modalities of targeting (remote sensing, telephone data)
• Technical adjustments in PMT some gains from quantile-based regression, machine
learning but not major improvements
• Examine degree to which PMT induces mis-reporting (India, Mexico)
• Degree to which self-selection models create distortions in labor supply (India, US,
Indonesia)
• Compare PMT based targeting/ community-based targeting (Indonesia, Liberia, India,
Malawi, Niger, Cameroon, Cote d’Ivoire) – generally less effective than PMT, though
some studies find preferred by community and much cheaper
• Future research needed:
• How to improve community-based targeting
• How to improve dynamic targeting
6. Motivations to discuss and research targeting
in Egypt
• Egypt experience can contribute to global literature and regional
literature
• Recent explosion of social protection programming in MENA region
• Policy goals in Egypt:
• Inform targeting of major national programs:
• Takaful and Karama
• Tamween
• Share lessons learned among NGOs to improve targeting of other
development programs
• Understand how Takaful/ Tamween targeting complements targeting by other
development interventions
8. اﻻﺳﺘﮭﺪاف
ﻣﺷﺎﻛل ﯾﺛﯾر ﻣﺎ أﻛﺛر اﻷرﺟﺢ ﻋﻠﻰ ھﻲ اﻻﺳﺗﮭداف ﻣﮭﻣﺔ
ﻣن
اﻻﺟﺗﻣ اﻟﺣﻣﺎﯾﺔ ﺑراﻣﺞ ﺗﺻﻣﯾم ﻗﺿﺎﯾﺎ ﻛل ﺑﯾن
ﺎﻋﯾﺔ
اﻟﺷﻣوﻟﯾﺔ ﻧﺣو ﺑﺎﻟﺗوﺟﮫ اﻟﻣﻧﺎداة إﻟﻰ ﻛﺛﯾرﯾن دﻓﻊ
universalism
وﺑﺘﻜﺮار
ﺑـ اﻟﻣﺗﻌﻠﻘﺔ اﻟﻘرارات ﻣﻧﺎﻗﺷﺔ
:
،اﻻﺳﺗﮭداف ﻧطﺎق اﺗﺳﺎع أو ﺿﯾﻖ ﻣدى
،اﻻﺳﺗﮭداف طرﯾﻘﺔ واﺧﺗﯾﺎر
و
اﻻﺳﺗﮭداف ﻧﺟﺎح ﻣدى ﻋﻠﻰ اﻟﺣﻛم
Steven Devereux. (2019). Targeting. in Schüring, Esther and Markus Loewe. (2021). Eds. Handbook on Social Protection Systems Edited by
Published by Edward Elgar Publishing Limited. https://www.researchgate.net/publication/353836100_Handbook_on_Social_Protection_Systems
Michal Rutkowski, Global Director, Social Protection and Jobs Global Practice, The World Bank Group in:
9. اﻟﻘﺮ ﯾﺴﺘﺤﻖ ﻛﺘﺎب
اءة
ﻓﻲ اﻟﻨﻈﺮ إﻋﺎدة
اﻟﻤﺴﺎﻋﺪات اﺳﺘﮭﺪاف
اﻻﺟﺘﻤﺎﻋﯿﺔ
:
ﻋﻠﻰ ﺟﺪﯾﺪة ﻧﻈﺮة
اﻟﻘﺪﯾﻤﺔ اﻟﻤﻌﻀﻼت
٥۳۹
ﺻﻔﺤﺔ
Grosh, Margaret, Phillippe
Leite, Matthew Wai-Poi,
and Emil Tesliuc, editors.
2022.
Revisiting Targeting in
Social Assistance: A New
Look at Old Dilemmas.
45. From: Brown, Ravaillon, and van de Walle (2016)
Correct
excluded
Correct
beneficiaries
Inclusion
error
Exclusion
error
• Exclusion error: proportion of poor who
are not included
• Inclusion error: proportion of included
who are not poor
• Coverage: the share of the population
targeted
46. From: Brown, Ravaillon, and van de Walle (2016)
Inclusion
error
Exclusion
error
• Exclusion error: proportion of poor who
are not included
• Inclusion error: proportion of included
who are not poor
• Coverage: the share of the population
targeted
Increasing cut-off decreases exclusion
error, increases share of population
receiving benefits, but increases
inclusion error
47. From: Brown, Ravaillon, and van de Walle (2016)
Correct
Correct
Inclusion
error
Exclusion
error
• Exclusion error: proportion of poor who
are not included
• Inclusion error: proportion of included
who are not poor
• Coverage: the share of the population
targeted
Increasing cut-off decreases exclusion
error, increases share of population
receiving benefits, but increases
inclusion error
The trade-off in exclusion error vs. inclusion error as
you change coverage rate depends on the where
you are in the distribution
If coverage rates are very low (orange) there can be
a lot of reduction in exclusion error without too
much concern about inclusion error increasing
48. From: Brown, Ravaillon, and van de Walle (2016)
Correct
Correct
Inclusion
error
Exclusion
error
• Exclusion error: proportion of poor
who are not included
• Inclusion error: proportion of included
who are not poor
More accurate targeting reduces trade-off between
exclusion error and inclusion error…
But may have higher data/ administrative costs